In security systems such as an entrance and exit management system and a system using access control, a technique of identifying the person to be identified and another person using physical features of the individual is known (biometrics personal authentication technique). One such technique is a method of authentication by face image. The method of authentication by face image is a method of authenticating the person to be authenticated by comparing the face image photographed with a camera etc. and a face image recorded in a database and the like. However, in the method of authentication by face image, high identification performance generally cannot be obtained with a method of computing similarity by simply overlapping the input image and the recorded image due to influence of direction of the face, illumination condition, date and time of photography, and the like.
A method referred to as Fisher Face method using linear discriminant analysis is used for a method of solving such problem (see e.g., non-patent document 1). The Fisher Face method is a method (linear discriminant analysis method) of assigning one class to each individual when there is a plurality of people so that the interclass variance becomes large and intraclass variance becomes small among a great number of people. This method excels in the checking performance using an actual image compared to the method of simply performing comparison between images.
As opposed to such method, a method referred to as kernel Fisher Face method for extracting the feature using kernel discriminant analysis is known as a method of enhanced identifying accuracy (see non-patent document 2). The kernel discriminant analysis is a method of once projecting the feature to a nonlinear space using a nonlinear kernel, and thereafter performing linear discriminant analysis, and generally, the identification performance is high compared to the Fisher Face method if the number of kernels is sufficiently large. However, since the amount of calculation necessary for feature extraction becomes larger in proportion to the number of kernels, it becomes a problem when applied to the actual system.
A method of applying a restraint condition such that the sum (L1 norm) of the absolute value of weight becomes a minimum, and using an optimum solution by using linear programming when obtaining the weight parameter in a feature space is proposed as a method of reducing the amount of calculation in the kernel discriminant analysis (see non-patent document 3). According to such method, it is proved from the result of the experiment that the number of kernels can be greatly reduced without lowering the identification performance. This method, however, can only be applied in the two class discriminant problem, and cannot handle a general multi-class discriminant problem.
A method of adaptively selecting only the kernel that is important in the discriminant is proposed as a method applicable to the multi-class problem and capable of reducing the amount of calculation (see non-patent document 4). However, as this method reduces the number of kernels, the amount of calculation of the same order as the kernel discriminant analysis is essentially required, and thus it is not suited for greatly reducing the amount of calculation.
A feature vector conversion technique for extracting a feature vector effective in discrimination from input pattern feature vectors, suppressing reduction of the feature amount effective in discrimination when compressing the feature order, and performing a more efficient feature extraction is proposed (see e.g., patent document 1).    Patent document 1: Japanese Laid-Open Patent Publication No. 2004-192603 (paragraphs 0045 to 0055)    Non-patent document 1: W. Zhao, R. Chellappa, P. J. Phillips, “Subspace Linear Discriminant Analysis for Face Recognition”, Tech. Rep. CAR-TR-914, Centre for Automation Research, (US), University of Maryland, College Park, 1999    Non-patent document 2: G. Baudat and F. Anouar, “Generalized Discriminant Analysis Using a Kernel Approach”, Neural Computation, (US), vol. 12, P.2385-2404, 2000    Non-patent document 3: S. Mika, G. Ratsch, and K. R. Muller. “A Mathematical Programming Approach to the Kernel Fisher Algorithm”, Advances in Neural Information Processing Systems 13, (US), 2001    Non-patent document 4: Xu, Y., Yang, J. Y., Lu, J., Yu, D. J., “An efficient renovation on kernel Fisher discriminant analysis and face recognition experiments”, Pattern Recognition, (US), vol. 37, No. 10, October 2004, p. 2091-2094